Teleseismic tomography: Equation one is wrong

ABSTRACT Seismic tomography methods that use waves originating outside the volume being studied are subject to bias caused by unknown structure outside this volume. The bias is of the same mathematical order and similar magnitude as the local-structure effects being studied; failure to account for it can significantly corrupt derived structural models. This bias can be eliminated by adding to the inverse problem three unknown parameters specifying the direction and time for each incident wave, a procedure analogous to solving for event locations in local-earthquake and whole-mantle tomography. The forward problem is particularly simple: The first-order change in the arrival time at an observation point resulting from a perturbation to the incident-wave direction and time equals the change in the time of the perturbed incident wave at the point where the unperturbed ray entered the study volume. This consequence of Fermat’s principle apparently has not previously been recognized. Published teleseismic tomography models probably contain significant artifacts and need to be recomputed using the more complete theory.

Antisite disorder and phase segregation in Mn2NiSn

A systematic study of crystal structure, local structure, magnetic and transport properties in quenched and temper annealed Ni2−xMn1+xSn alloys indicate the formation of Mn3Sn type structural defects caused by an antisite disorder between Mn and Sn occupying the Y and Z sublattices of X2YZ Heusler structure. The antisite disorder is caused by the substitution of Ni by Mn at the X sites. On temper annealing, these defects segregate and phase separate into L21 Heusler and D019 Mn3Sn type phases.

Local Structure Awareness-Based Retinal Microaneurysm Detection with Multi-Feature Combination

Retinal microaneurysm (MA) is the initial symptom of diabetic retinopathy (DR). The automatic detection of MA is helpful to assist doctors in diagnosis and treatment. Previous algorithms focused on the features of the target itself; however, the local structural features of the target and background are also worth exploring. To achieve MA detection, an efficient local structure awareness-based retinal MA detection with the multi-feature combination (LSAMFC) is proposed in this paper. We propose a novel local structure feature called a ring gradient descriptor (RGD) to describe the structural differences between an object and its surrounding area. Then, a combination of RGD with the salience and texture features is used by a Gradient Boosting Decision Tree (GBDT) for candidate classification. We evaluate our algorithm on two public datasets, i.e., the e-ophtha MA dataset and retinopathy online challenge (ROC) dataset. The experimental results show that the performance of the trained model significantly improved after combining traditional features with RGD, and the area under the receiver operating characteristic curve (AUC) values in the test results of the datasets e-ophtha MA and ROC increased from 0.9615 to 0.9751 and from 0.9066 to 0.9409, respectively.

New Robust Projection to Latent Structure

In many actual nonlinear systems, especially near the equilibrium point, linearity is the primary feature and nonlinearity is the secondary feature. For the system that deviates from the equilibrium point, the secondary nonlinearity or local structure feature can also be regarded as the small uncertainty part, just as the nonlinearity can be used to represent the uncertainty of a system (Wang et al. 2019). So this chapter also focuses on how to deal with the nonlinearity in PLS series method, but starts from an different view, i.e., robust PLS. Here the system nonlinearity is considered as uncertainty and a new robust $$\mathrm{L}_1$$ L 1 -PLS is proposed.